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Refraction.dev
✓ verifiedFreemium
AI coding assistant for editors and IDEs that explains, refactors, documents, and generates code across 56 languages.
2.8K visits/mo
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Debugg AI
✓ verifiedFreemium
Zero-config AI browser testing that auto-runs end-to-end tests on every GitHub PR and posts results as comments.
3.0K visits/mo6.3K saves
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devActivity
✓ verifiedFreemium
GitHub-based engineering analytics that tracks contributions, automates performance reviews and adds gamification for dev teams.
52K visits/mo
Pricing
No public pricing
Hobby: Free (10 code generations, 1 user)
Pro: $8/mo (unlimited generations, editor extensions)
Team: $14/user/mo (multiple members, shared history)
Free trial available
Free: $0 (public repos, 100 tests/mo)
Pro: $20/mo (private repos, 1,000 tests/mo)
Grow: $40/mo (5,000 tests/mo)
Free: $0/contributor (up to 7 contributors, 90-day retention)
Premium: $10/contributor (unlimited contributors, AI insights)
No public pricing
Core features
- ✦Fast tensor operations
- ✦Differentiable tensors for gradient-based optimization
- ✦Network connectivity
- ✦Integration with Bun and Flashlight
- ✦Support for GPU computation with CUDA (Linux) and CPU computation (macOS)
- ✦Bug detection and fix suggestions
- ✦Code and CSS framework conversion
- ✦Unit test and documentation generation
- ✦Regex, SQL query, and CI/CD pipeline generation
- ✦Code explanation and style checking
- ✦Editor extensions for VS Code, Sublime, JetBrains, Visual Studio
- ✦No-config automated browser testing
- ✦GitHub-native PR testing with inline results
- ✦Fully managed cloning, build, and tunneling
- ✦AI app mapping and targeted test generation
- ✦Recorded, replayable test sessions
- ✦MCP server for Claude and Codex
- ✦Contribution and work-quality analytics
- ✦Automated, AI-powered performance reviews
- ✦Retrospective insights
- ✦Operational bottleneck alerts
- ✦Gamification with XP, levels and leaderboards
- ✦Uses Git metadata without accessing source code
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Use cases
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Generating unit tests for existing functions
- →Refactoring legacy code to modern practices
- →Producing inline documentation automatically
- →Learning new programming languages or concepts via AI explanations
- →Catch UI regressions before merge
- →Test user flows automatically on each PR
- →Validate flows against a local dev server
- →Replace hand-written Playwright/Selenium suites
- →Automating developer performance reviews
- →Spotting delivery bottlenecks
- →Generating retrospective insights
- →Motivating teams via gamification
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